In [1]:
import io
import spotipy

import numpy as np

import matplotlib
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors

import os

import pandas as pd

from collections import Counter, OrderedDict
from spotipy.oauth2 import SpotifyOAuth
import requests

import plotly.graph_objects as go
import plotly.express as px

from IPython.display import Image
In [2]:
scope = 'user-top-read'
sp = spotipy.Spotify(auth_manager=SpotifyOAuth(scope=scope,open_browser=False))
In [3]:
my_playlists = sp.current_user_playlists(limit=50, offset=0)

terms = ['short_term', 'medium_term', 'long_term']

all_genres = []
all_names = []

for i in range(3):
    top50 = sp.current_user_top_artists(limit=20,offset=0,time_range=terms[i])

    for j, item in enumerate(top50['items']):
        all_genres += item['genres']
        all_names += [item['name']]
In [4]:
def CountFrequency(my_list):
 
    freq = {}
    for item in my_list:
        if (item in freq):
            freq[item] += 1
        else:
            freq[item] = 1
        
    return freq

It seems like I mostly listen to pop, rap, r&b, and k-pop.¶

I'm surprised that rap comes 2nd in this list as I thought I listen to more r&b on a regular basis. I suspect that what I think is r&b is labelled as rap by Spotify.¶

In [5]:
genre_freq = CountFrequency(all_genres)

gdf = pd.DataFrame(genre_freq,index=['number']).T.sort_values(by='number',ascending=False)

gdf.head(7)

fig = px.pie(gdf, values='number', names=gdf.index)
fig.show()

Data shows that I don't really favor a particular subset of artists.¶

In [6]:
names_freq = CountFrequency(all_names)

ndf = pd.DataFrame(names_freq,index=['number']).T.sort_values(by='number',ascending=False)

ndf.head(7)

fig1 = px.pie(ndf, values='number', names=ndf.index)
fig1.show()
In [7]:
sml = []
pop = [],[],[]
artist = [],[],[]
pop_score = 0

for i in range(3):

    results = sp.current_user_top_artists(time_range=terms[i], limit=10)

    for j, item in enumerate(results['items']):
        popularity = item['popularity']
        pop_score += popularity
        artist[i].append(item['name'])
        pop[i].append(popularity)
    sml.append(popularity)

plt.rcParams["figure.figsize"] = (20,3)

for i in range(3):
    print(terms[i])
    print(artist[i])
    print("-"*10)
    df = pd.DataFrame({"x" : pop[i]})

    cmap = mcolors.LinearSegmentedColormap.from_list("", ["grey", "pink", "purple"])
    
    plt.bar(artist[i], df["x"], color=cmap(df.x.values/100))
    plt.xlabel('Artists', labelpad=12)
    plt.xticks(rotation=90)
    
    plt.title("Top 10 Artists ("  + terms[i] + ")")
    plt.show()

short_term
['Drake', 'PARTYNEXTDOOR', 'The Weeknd', 'Future', 'Jay Park', 'Pop Smoke', 'SZA', 'Chris Brown', 'Kehlani', 'Khalid']
----------

medium_term
['Drake', 'The Weeknd', 'PARTYNEXTDOOR', 'Future', 'Bryson Tiller', 'Khalid', 'LE SSERAFIM', 'Chase Atlantic', 'Chris Brown', 'Giveon']
----------

long_term
['Red Velvet', 'The Weeknd', 'Drake', 'Future', 'Bryson Tiller', 'Ariana Grande', 'PARTYNEXTDOOR', 'IU', 'Khalid', 'Leessang']
----------
In [8]:
artists = [],[],[]
tracks = [],[],[]
track_pop = [],[],[]

for i in range(3):
    results = sp.current_user_top_tracks(limit=10,offset=0,time_range=terms[i])
    
    text = ""
        
    for j, item in enumerate(results['items']):
        popularity = item['popularity']
        track = item['name']
        track_pop[i].append(popularity)
        tracks[i].append(track)
        
        target = item['artists']
        name = target[0]
        artist = name['name']
        artists[i].append(artist)
        
    df2 = pd.DataFrame({"x" : track_pop[i]})

    cmap = mcolors.LinearSegmentedColormap.from_list("", ["lightsteelblue", "cornflowerblue", "blue"])
    
    plt.bar(tracks[i], df2["x"], color=cmap(df2.x.values/100))
    plt.xlabel('Tracks', fontsize=10)
    plt.xticks(rotation=90)
    plt.title(" Top Tracks (" + terms[i] + ")")
    plt.show()

/home/devillish_red/spotify-data-analysis/myenv/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning:

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In [9]:
colors = ['pink','tan','cornflowerblue']

for i, artist in enumerate(artists):
    df3 = pd.DataFrame(artist)
    c = Counter(df3[0])
    y = OrderedDict(c.most_common())
    df4 = pd.DataFrame({"x" : c})
    plt.bar(c.keys(), y.values(),color=colors[i])
    plt.xticks(rotation=90)

    plt.xlabel('Artists', fontsize=10)
    plt.title("Songs per Artist in Top 50 (" + terms[i] + ")")
    plt.show()
    if i == 2:
        buf = io.BytesIO()
        plt.savefig(buf, format = 'png')
        buf.seek(0)
    

<Figure size 2000x300 with 0 Axes>
In [ ]: